Convergence Analysis of Adaptive Full-Rank and Multi-Stage Reduced-Rank Interference Suppression

摘要

The performance of adaptive fullrank and Multi-Stage (MS) reduced-rank interference suppression is analyzed for Direct-Sequence (DS)-Code Division Multiple Access (CDMA) with random spreading sequences. Least Squares (LS) adaptive algorithms are considered with training and without training (blind LS). We compute the large system limit of output Signal-to-Interference-plus-Noise-Ratio (SINR) as a function of normalized observations, i.e., the number of users K, the processing gain N, and the number of training symbols or observations i all tend to infinity with fixed ratios alpha=K/N and eta-i/N. Numerical results shwo that the large system limits accurately predict the simulated convergence performance of the algorithms with moderate spreading gain (e.g., N=32). Our results also show that the adaptive MS filters converge significantly faster than the analogous full-rank filters.